چكيده به لاتين
The application of the Coarse Sun Sensors (CSS) and Magnetometers in the Attitude Determination and Control System (ADCS) of the small satellites is undeniable due to the some characteristics such as simplicity, cheapness and lightness. Beside the mentioned merits, the non-ignorable error of the sensors causes some limitation to be used in different missions. To solve that, in this thesis, some new methods for calibration of the CSS and magnetometer is suggested and then the advantage of the given methods on the improvements of the ADCS is investigated when using model predictive control (MPC) and QUEST algorithm as the control and estimation algorithm, respectively. The main challenges of the CSS are Field of View (FOV) limitation, inaccurate sun vector estimation and nonlinear behavior of the error distribution through the FOV. In this thesis, we propose some solution for the pointed challenges. To do that, we introduce an iterative algorithm based on the normal cell vector and solar cell characteristic calibration to improve the accuracy of the CSS. In this procedure, we calibrate the normal cell vectors coming from production stage as one of the novelties of this thesis. Furthermore, the given algorithm makes it possible to calibrate misalignment of the sun sensor in onboard calibration scheme. On the other hand, for magnetometer calibration, the on-board and ground based calibration is suggested in two separated point of view. The ground based calibration is given to answer the questions of “How many test data is suitable in the calibration scenario?” and “What is the optimal data distribution?”. In the on-board calibration scheme, we try to calibrate the reaction wheel and solar panel effects on the magnetometer. Overall, estimation of the scale factor parameter, constant bias parameter, and coefficient of reaction wheels current and coefficient of solar panel currents is given. After that, using accurate attitude knowledge, we can estimate the non-orthogonally matrix using least square algorithm.
